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dc.contributor.author조인휘-
dc.date.accessioned2019-08-06T05:08:22Z-
dc.date.available2019-08-06T05:08:22Z-
dc.date.issued2019-02-
dc.identifier.citationCommunications in Computer and Information Science, v. 931, Page. 414-423en_US
dc.identifier.isbn978-981135906-4-
dc.identifier.isbn978-981-13-5907-1-
dc.identifier.issn1865-0929-
dc.identifier.urihttps://link.springer.com/chapter/10.1007%2F978-981-13-5907-1_44-
dc.identifier.urihttp://repository.hanyang.ac.kr/handle/20.500.11754/108274-
dc.description.abstractThe industry 4.0 and Industrial IoT is leading new industrial revolution. Industrial IoT technologies make more reliable and sustainable products than traditional products in automation industry. Industrial IoT devices transfer data between one another. This concept is need for advanced connectivity and intelligent security services. We focus on the security threat in Industrial IoT. The general security systems enable to detect normal security threat. However, it is not easy to detect anomaly threat or network intrusion or new hacking methods. In the paper, we propose autoencoder (AE) using the deep learning based anomaly detection with invasion scoring for the smart factory environments. We have analysis F-Score and accuracy between the Density Based Spatial Clustering of Applications with Noise (DBSCAN) and the autoencoder using the KDD data set. We have used real data from Korea steel companies and the collected data is general data such as temperature, stream flow, the shocks of machines, and etc. Finally, experiments show that the proposed autoencoder model is better than DBSCAN. © Springer Nature Singapore Pte Ltd. 2019.en_US
dc.description.sponsorshipThis work was supported by the Technology development Program (S2521883) funded by the Ministry of SMEs and Startups (MSS, Korea).en_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.subjectAnomaly detectionen_US
dc.subjectIntrusion detectionen_US
dc.subjectScoringen_US
dc.subjectAutoencoderen_US
dc.subjectDBSCANen_US
dc.subjectSmart factoryen_US
dc.subjectIndustrial IoTen_US
dc.titleAutoencoder-Based Anomaly Detection with Intrusion Scoring for Smart Factory Environmentsen_US
dc.typeArticleen_US
dc.relation.no1-
dc.relation.volume931-
dc.identifier.doi10.1007/978-981-13-5907-1_44-
dc.relation.page414-423-
dc.relation.journalCommunications in Computer and Information Science-
dc.contributor.googleauthorBae, Gimin-
dc.contributor.googleauthorJang, Sunggyun-
dc.contributor.googleauthorKim, Minseop-
dc.contributor.googleauthorJoe, Inwhee-
dc.relation.code2019013730-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF COMPUTER SCIENCE-
dc.identifier.pidiwjoe-
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COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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